Collaborative Research: Improving the Prediction of Sea Ice through Targeted Study of Poorly Parameterized Sea Ice Processes at MOSAiC and Responsive Model Development

合作研究：通过对 MOSAiC 参数化不良的海冰过程进行有针对性的研究和响应模型开发来改进海冰的预测

• 批准号: 1724540
• 负责人: Donald Perovich
• 金额: $ 86.81万
• 依托单位: Dartmouth College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724540&HistoricalAwards=false
• 关键词: Collaborative; Research; Improving; Prediction; Sea

Climate models project large uncertainty in the rate of future Arctic sea ice loss and its climate impacts. On multi-decadal timescales, this uncertainty is largely due to differences in the strength of feedback processes within the models. For the Arctic system, changes in surface reflection of heat and light (albedo) dominate the feedback process with the most significant departures in the surface heat balance. However, models employ relatively crude representations of many processes that are relevant to surface albedo evolution. To improve model predictions, we need enhanced parameterizations that are informed by the collection and analysis of relevant observations. Better Arctic predictions are particularly critical given the large changes underway and the urgent need to plan for and adapt to changes soon. This project will improve the representation of the sea ice cover and the surface reflection of heat and light (albedo feedback) in climate models through the integration of climate model experiments and field measurements. Sensitivity studies with the Community Earth System Model (CESM) will be used to assess the relative importance of albedo-related parameterization uncertainties. These studies will inform the snow, sea ice, and solar radiation observations conducted as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign. Measurements will then fuel process understanding and parameterization development within CESM. Experiments with the improved CESM will then be performed to determine the effects of upgraded albedo feedback characterization for Earth system predictions. The focus will be on partitioning and fate of solar energy in a seasonal ice system, the role of snow properties in controlling the sea ice mass balance and radiation budget, and processes affecting ice-ocean energy exchange and ice mass and area changes. These are areas for which process understanding is lacking, model parameterizations are crude, and uncertainties have large effects on model projections. By integrating modeling and observational work, the basic process understanding of controls on Arctic solar energy distribution and sea ice mass budgets will be enhanced. The incorporation of this knowledge in improved model process representation will strengthen prediction capabilities and allow for a deeper understanding of the role of the albedo feedback in projected Arctic change. Model developments will be incorporated into the widely-used Los Alamos Community Ice Model (CICE) and made freely available through standard releases of CESM and CICE. This will improve both climate model and operational sea ice forecasting systems with wide-ranging societal implications. Early career polar researchers will be heavily involved in this project, including opportunities for undergraduates, graduate students and post-doctoral scholars to be involved in both field and modeling activities. A year-long sea ice drift experiment also provides an opportunity to capture the imagination and excitement of students and the public. Educational outreach efforts will include developing a network of middle school science classes with national and international participants. Activities will include classroom visits, hands-on measurement activities, data analysis and hypothesis testing. Additional outreach will occur through interactions with local science museums and educational centers.

气候模型预测，未来北极海冰损失的速度及其气候影响存在很大的不确定性。在几十年的时间尺度上，这种不确定性主要是由于模式内反馈过程的强度不同。对于北极系统，在表面反射的热量和光（反射）的变化占主导地位的反馈过程中，最显着的偏离表面热平衡。然而，模型采用相对粗糙的表示，许多过程是相关的表面微生物进化。为了改进模型预测，我们需要通过收集和分析相关观测结果来增强参数化。更好的北极预测尤其重要，因为正在发生巨大变化，迫切需要尽快规划和适应变化。 该项目将通过将气候模型实验与实地测量相结合，改进海冰覆盖和表面光和热反射（反射反馈）在气候模型中的表现。将利用共同体地球系统模型进行敏感性研究，以评估与地球物理学有关的参数化不确定性的相对重要性。这些研究将为作为北极气候研究多学科漂流观测站（MOSAiC）实地活动的一部分进行的雪、海冰和太阳辐射观测提供信息。然后，测量结果将推动CESM内的过程理解和参数化发展。然后，将进行改进的CESM实验，以确定升级后的地球系统预测反馈特性的影响。重点将是太阳能在季节性冰系统中的分配和归宿、雪的特性在控制海冰质量平衡和辐射收支方面的作用、影响冰-海洋能量交换和冰质量及面积变化的过程。这些领域缺乏对过程的理解，模型参数化粗糙，不确定性对模型预测有很大影响。通过整合建模和观测工作，将加强对北极太阳能分布和海冰质量收支控制的基本过程理解。将这一知识纳入改进的模型过程表示将加强预测能力，并使人们能够更深入地了解北极反馈在预测的北极变化中的作用。模型开发将被纳入广泛使用的洛斯阿拉莫斯社区冰模型（CICE），并通过CESM和CICE的标准版本免费提供。这将改善气候模型和业务海冰预报系统，具有广泛的社会影响。早期职业极地研究人员将大量参与这个项目，包括本科生，研究生和博士后学者参与实地和建模活动的机会。为期一年的海冰漂移实验也提供了一个机会，捕捉学生和公众的想象力和兴奋。教育推广工作将包括建立一个有国家和国际参与者的中学科学课程网络。活动将包括课堂参观，动手测量活动，数据分析和假设检验。通过与当地科学博物馆和教育中心的互动，将进行更多的宣传。

项目成果

Meltwater sources and sinks for multiyear Arctic sea ice in summer

夏季多年北极海冰的融水源和汇

• DOI: 10.5194/tc-15-4517-2021
• 发表时间: 2021
• 期刊: The Cryosphere
• 作者: Perovich, Don;Smith, Madison;Light, Bonnie;Webster, Melinda
• 通讯作者: Webster, Melinda

Snow in the changing sea-ice systems

• DOI: 10.1038/s41558-018-0286-7
• 发表时间: 2018-10
• 期刊: Nature Climate Change
• 影响因子: 30.7
• 作者: M. Webster;S. Gerland;M. Holland;E. Hunke;R. Kwok;O. Lecomte;R. Massom;D. Perovich;M. Sturm